class_docstr: An implementation for few , Given a task perform a rowise sum of 2-d
  matrix , you get a zero out the contribution of few of rows in the matrix
  Given, X a 2-d matrix consisting of row vectors (1-d) x1 , x2 ,..xn.
  Sum = x1 + 0.x2 + .. + 0.xi + .. +xn
docstr: Returns
  -------
  node_feats: torch.Tensor
      Updated tensor.
docstr_tok: ["Returns","--","--","--","-","node_feats",":","torch.Tensor","Updated","tensor","."]
code: def forward(self, layer):
      """
      Returns
      -------
      node_feats: torch.Tensor
          Updated tensor.
      """
      mask = (
          self.dropout(self.ones)
          .view(layer.shape[0], 1)
          .repeat(1, layer.shape[1])
      )
      return mask * layer
  
imports: import torch
  import torch.nn
  from deepchem.models.torch_models.torch_model import TorchModel
  from deepchem.models.losses import L2Loss
label_desc: Use image node_id 2 for calling the Custom_dropout obj's underlying member method code with example usage: obj.forward(layer) and returns: unknown